Research in Rural Sociology and Development
Bibliographic record
Abstract
Citation (2010), "Research in Rural Sociology and Development", Milbourne, P. (Ed.) Welfare Reform in Rural Places: Comparative Perspectives (Research in Rural Sociology and Development, Vol. 15), Emerald Group Publishing Limited, Bingley, p. ii. https://doi.org/10.1108/S1057-1922(2010)0000015015 Publisher: Emerald Group Publishing Limited Copyright © 2010, Emerald Group Publishing Limited Book Chapters Research in Rural Sociology and Development Research in Rural Sociology and Development Copyright page List of Contributors Chapter 1 Scaling and spacing welfare reform: making sense of welfare in rural places Chapter 2 Impacts of welfare reform on rural people and places in the United States Chapter 3 Devolution, social exclusion, and spatial inequality in U.S. welfare provision Chapter 4 Color-blind welfare reform or new cultural racism? Evidence from rural Mexican- and Native-American communities Chapter 5 Social welfare policies and rural Canada Chapter 6 Placing welfare in rural England Chapter 7 Rural welfare to work in Wales: young people's experiences Chapter 8 Giving up farming and the welfare state restructuration in Finland Chapter 9 Shifting welfare, shifting people: rural development, housing and population mobility in Australia Chapter 10 Australia's rural welfare policy: overlooked and demoralised Chapter 11 School closures as breaches in the fabric of rural welfare: community perspectives from New Zealand
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".